# train
# python main.py --mode='train_and_eval'  \
#                --training_file_pattern=tfrecord/train*  \
#                --hparams="use_bfloat16=false" \
#                --use_tpu=False  \
#                --validation_file_pattern=tfrecord/test*  \
#                --val_json_file=dataset/test.json  \
#                --model_dir=model/efficientdet-d0/ \
#                --backbone_ckpt=ckpt/noisy_student_efficientnet-b0  | tee train_B0.log

# 有效
#python main.py --mode='train'  \
#               --training_file_pattern=tfrecord/train*  \
#               --hparams="use_bfloat16=false,num_classes=2" \
#               --use_tpu=False  \
#               --model_dir=model/efficientdet-d0/ \
#               --backbone_ckpt=ckpt/noisy_student_efficientnet-b0


# eval
# ssuming /tmp/efficientnet-d0/ contains your checkpoint.
python main.py --mode=eval \
              --ckpt_path=model/efficientnet-d0/ \
              --validation_file_pattern=tfrecord/test* \
              --val_json_file=dataset/test.json \
              --hparams="use_bfloat16=false,num_classes=2" \
              --use_tpu=False


# inference
# pip install pytype pycocotools
#python model_inspect.py --runmode=infer \
#                        --model_name=efficientdet-d0 \
#                        --ckpt_path=model/efficientdet-d0/ \
#                        --input_image=testdata/xianchang/*.jpg \
#                        --output_image_dir=result_xianchang/
